Attribute Value Weighting in K-Modes Clustering
首发时间:2007-01-12
Abstract:In this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.
keywords: Clustering, Categorical Data, K-Means, K-Modes, Data Mining
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Attribute Value Weighting in K-Modes Clustering
摘要:In this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.
关键词: Clustering, Categorical Data, K-Means, K-Modes, Data Mining
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No.1069713411116857****
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